# Entropy-Driven Curriculum for Multi-Task Training in Human Mobility Prediction

arXiv: https://arxiv.org/abs/2509.01613 · HTML: https://mlsanity.com/paper/2509.01613/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Future Directions in Human Mobility Science](https://mlsanity.com/paper/2408.00702/) (2024)
- [Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax](https://mlsanity.com/paper/2212.05707/) (2022)
- [GEO-BLEU: Similarity Measure for Geospatial Sequences](https://mlsanity.com/paper/2112.07144/) (2021)
- [Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout](https://mlsanity.com/paper/2211.15182/) (2022)
- [Metropolitan Scale and Longitudinal Dataset of Anonymized Human Mobility Trajectories](https://mlsanity.com/paper/2307.03401/) (2023)
- [Llama-Mob: Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility Prediction](https://mlsanity.com/paper/2410.23692/) (2024)
- [GeoFormer: Predicting Human Mobility using Generative Pre-trained Transformer (GPT)](https://mlsanity.com/paper/2311.05092/) (2023)
- [Personalized human mobility prediction for HuMob challenge](https://mlsanity.com/paper/2310.12900/) (2023)

## Built on by (most cited first, 1 of 1)

- [What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical Reasoning](https://mlsanity.com/paper/2510.19099/) (2025)

## Abstract (arXiv)

The increasing availability of big mobility data from ubiquitous portable devices enables human mobility prediction through deep learning approaches. However, the diverse complexity of human mobility data impedes model training, leading to inefficient gradient updates and potential underfitting. Meanwhile, exclusively predicting next locations neglects implicit determinants, including distances and directions, thereby yielding suboptimal prediction results. This paper presents a unified training framework that integrates entropy-driven curriculum and multi-task learning to address these challenges. The proposed entropy-driven curriculum learning strategy quantifies trajectory predictability based on Lempel-Ziv compression and organizes training from simple to complex for faster convergence and enhanced performance. The multi-task training simultaneously optimizes the primary location prediction alongside auxiliary estimation of movement distance and direction for learning realistic mobility patterns, and improve prediction accuracy through complementary supervision signals. Extensive experiments conducted in accordance with the HuMob Challenge demonstrate that our approach achieves state-of-the-art performance on GEO-BLEU (0.354) and DTW (26.15) metrics with up to 2.92-fold convergence speed compared to training without curriculum learning.